CORTEXA
← Browse
arxivcs.NIcs.RO2026-07-11

CSI-Assisted Edge SLAM Testbed Platform for 5G Connected Unmanned Autonomous Vehicles

Boris Radovanovic, Sasa Talosi, Srdjan Sobot, Dejan Vukobratovic

The evolution from 5G towards 6G reinforces interest in connected robotics, where mobile robots offload compute-intensive tasks to edge servers over ultra-reliable low-latency communication (URLLC) links. Simultaneous localization and mapping (SLAM), a fundamental yet demanding robotics function, is increasingly considered for edge deployment within mobile edge computing (MEC) frameworks. In parallel, integrated sensing and communications (ISAC) enables the use of radio channel information, such as channel state information (CSI), as an additional sensing modality in radio-based SLAM. In this paper, we design and implement a CSI-assisted Edge SLAM testbed integrating a custom unmanned ground vehicle (UGV), a ROS2-based SLAM framework, and a 5G Open Radio Access Network (O-RAN) system. The proposed architecture provides an end-to-end, cross-layer view of ROS2 sensor data streaming over 5G, explicitly enabling CSI exposure and integration into the SLAM pipeline. We analyze ROS2 DDS communication, RTPS packetization, and 5G user-plane transport, and discuss mechanisms for CSI extraction and delivery via O-RAN components. The platform enables realistic experimentation with communication-aware SLAM and reveals key challenges related to latency, data streaming, synchronization, and cross-system integration, providing insights for future 6G-enabled robotic platforms.

View free PDFSource page

Related papers

arxivcs.NIcs.RO2026-07-08

How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles

Amir Mohammadisarab, Miguel Sepulcre, Luca Lusvarghi, Javier Gozalvez

Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line-of-sight is obstructed. Cooperative perception (or collective perception) is expected to miti…

View free PDFSource page
arxivcs.ROcs.AIcs.LGcs.NIeess.SY2026-07-21

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, et al.

The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strate…

View free PDFSource page
arxivcs.ROcs.NI2026-07-11

Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving

Shihao Zhang, Jing Yang, Ziyu Song, Zheng Lin, Sunil Prajapat, Zhaochen Xia, et al.

Autonomous driving planning is a key component of IoT-enabled intelligent transportation systems, requiring vehicles to generate safe, efficient, and executable trajectories in complex urban environments from multi-source contextual information. While imitation learning (IL) has…

View free PDFSource page
arxivcs.ROcs.AIcs.NIeess.SY2026-07-17

MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation

Rajat Bhattacharjya, Hyeonjong Ju, Sing-Yao Wu, Eli Bozorgzadeh, Nikil Dutt

Communication-limited robots in mission-critical scenarios such as disaster inspection and search-and-rescue must make reliable onboard decisions without access to remote operators or high-capacity reasoning services. Episodic memory reuse is an attractive low-cost fallback, but…

View free PDFSource page
arxivcs.ROcs.LG2026-07-02

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran

Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment…

View free PDFSource page